A Framework for Statistical Analysis of Student Engagement in OBE-based Classrooms Environment Using YOLO Model
DOI:
https://doi.org/10.57041/vol5iss1pp1-10Keywords:
Outcome-based education (OBE), YOLO (You Only Look Once), convolutional neural networks (CNNs), machine learning (ML), deep learning (DL), contextual attention (CA)Abstract
The most important aspect of effective learning in an Outcome-Based Education (OBE) classroom environment is student involvement and attentiveness during the lecture. This study examines various student behaviours and evaluates the attention of students during lectures using deep learning techniques, YOLO v8. The classroom representative visuals were perceived from an online available dataset containing students’ images that have been recorded during the lecture. These photos are subjected to YOLO, which classifies different student activities into those that contribute positively or negatively to attention in the classroom. Positive markers of attention include actions like raising hands, concentrating on the front, reading, writing, and interacting with the teacher. On the other hand, distractions like eating, drinking, using a phone, or seeming drowsy have a detrimental impact. The results assist teachers in enhancing their teaching methodologies and offer insights into patterns of classroom involvement.
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